Transcript A Causal Analytics Framework to integrate Social Media Content

Causal Analytics with Social Media
Content
Lipika Dey
Innovation Labs, Delhi
1
Agenda
Agenda
Introduction to Innovation Labs
Social media content for business
analytics
Behavioral analysis
Future Directions
2
2
2
TCS R&D – Innovation Labs
 ~ 600 people; 60 PhDs.
• Data Analytics - Text Mining, Enterprise
Information Fusion, Big Data Management
• Software Architecture
• Graphics & Virtual Reality
• Multimedia Applications & Computer Vision
• Natural Language Processing
• Speech Technology
• Performance Engineering
DELHI
KOLKATA
• Analytics & Data Mining
• Large Scale Systems
• Software Engineering Tools
MUMBAI
PUNE
HYDERABAD
BANGALORE
• Embedded Systems (VLSI)
• Wireless (WiMAX, 4G, RFID)
• Signal Processing
• Data Security (PKI, ECDSA)
• Life Sciences (Bioinformatics)
CHENNAI
• Green IT (Power Management)
3
3
3
Social media based analytics
Content
Analysis
Sentiment
Analysis
Causal
Analytics
Social Network
Analysis
4
Social-media Intelligence
Issues and
Opportunities
Impact on
business
Social Media
Events
Context to
interpret
Business
Data
5
The Retail Story
Shampoo sales are going down
Market basket Analysis – Shampoo sells with milk and bread
Milk and bread sell as quick-refill – sales showed decline
Survey conducted – are you satisfied with quality / price / availability of milk and bread
More positive than negative
?
?
?
6
Seek answer in social-media
7
Goal-driven text analytics
Action
Knowledge Acquisition
Recommendation /
Prediction
Domain knowledge
Business Knowledge
Business units
Business Goals
Feedback /
Impact
• Prioritization of issues
• Business Case
Business Analysis
• Key drivers for satisfaction /
dissatisfaction / problems
• Segment-wise reports
Causal Analysis
•
•
•
•
Customer Pain-points and Delights
Events
Trends
Frequent patterns
Knowledge-driven Analytics
Mapping issues to goals
Measuring Performance Indicators
Text Mining
Consumer-generated Text
8
Text Analytics Process
Expect
Improvement in
performance
Gather
Mine
Map
Interpret
Improve
Analysis
KPIs to measure process performance
Domain Ontology
Semi-supervised Fuzzy Clustering
Content Classification
Mapping
Text components to Business Processes
Mining
Text elements - terms extracted from
Consumer –generated Unstructured Text
Cleaning and pre-processing
Natural Language Processing
Statistical Text Processing
Involvement of business expert
Knowledge-base
9
Reports for the Retail Store
Discovering New issues
Relevance / representativeness
Anomaly detection
Novelty of issues
Divergence of issues across stores/regions/products/categories
Learn from correlations
10
Content Discovery and Analytics
 Semi-supervised fuzzy Clustering
– Seeded clustering
– Learn domain terms
 Topic Discovery (Latent Dirichlet Allocation)
–
–
–
–
Topic novelty
Topic spread
Topic affinity
Topic relevance
 Event detection
– Entity and action-oriented (Conditional Random Fields)
– Event linking – story building
11
Topic discovery – iPhone related tweets
Features
Comparison
TV show
Gifts
12
Influence-driven analysis
13
Learning to identify relevant content
Event detection
Entity Detection
Address resolution
Raise alarm
Throw alerts
WSDM - 2012
14
Learning cause-effect relationships
Reinforcement learning framework to learn critical events
15
Topic spread – Topic evolution – Topic affinity
Event history – India
Olympics - 2012
IJCAI 2011 – From News
From Twitter -To be presented at WI - 2012
16
Behavioral Analysis on Social Networks
Regular,
Irregular,
Problem
Consistent, Low
Inconsistent
Clustering and characterizing
volumeactivity patterns
Extreme users based on their
Volume, Regularity, Consistency
Use
Provides insights about different categories of users
(i). Trade Promoters
Regular,
Periodic, Consistent,
(ii).
News Agencies
Consistent,
Low volume
Medium
volume
(iii).
Analysts
(iv). Regular users
(v). Spammers
Predict actions and information flow
Less regular,
consistent, Low volume
Methodology
Regular, consistent,
Irregular, inconsistent,
A wavelet-based clustering
mechanism
that
groups
High volume
Low volumeusers according
to their temporal activity profiles
(To be presented at ICPR 2012, Japan)
17
Interested in
 Content-based analytics
– Supply-chain models revisited
– Demand forecasting
 Identity resolution across social-media
– Social CRM
 Fraud detection
– Spammers
– Fake identities
 Information diffusion
– Across regions
– Interestingness
– Effect
 Intent mining
18
19